Fashion preference analysis

ABSTRACT

A machine is configured to determine fashion preferences of users and to provide item recommendations based on the fashion preferences. For example, the machine accesses an indication of a fashion style of a user. The fashion style is determined based on automatically captured data pertaining to the user. The machine identifies, based on the fashion style, one or more fashion items from an inventory of fashion items. The machine generates one or more selectable user interface elements for inclusion in a user interface. The one or more user interface elements correspond to the one or more fashion items. The machine causes generation and display of the user interface that includes the one or more selectable user interface elements. A selection of a selectable user interface element results in display of a combination of an image of a particular fashion item and an image of an item worn by the user.

CLAIM OF PRIORITY

This application is a continuation of U.S. application Ser. No.14/326,125, filed Jul. 8, 2014, which claims the benefit of priority ofU.S. Provisional Patent Application No. 61/981,011, filed Apr. 17, 2014,each of which is hereby incorporated by reference in its entirety.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright eBay, Inc. 2014, All Rights Reserved.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to the processingof data. Specifically, the present disclosure addresses systems andmethods to determine fashion preferences of users and to provide itemrecommendations to the users based on the users' fashion preferences.

BACKGROUND

Online commerce has changed the way people shop. Many people purchase avariety of products from online stores, such as electronics, groceries,books, and cars. One of the most impressive success stories of onlinecommerce is the fashion apparel segment. This may be attributed at leastin part to the variety of fashion choices afforded by large-scaleinventories at competitive prices. However, with the advantages offeredby hosting large scale fashion inventories, comes the need to facilitateeffective interactions by users with millions of item listings. Many ofthe existing e-commerce sites utilize text-based search engines thathelp identify the products most related to the query.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings.

FIG. 1 is a network diagram depicting a client-server system, withinwhich some example embodiments may be deployed.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications and that, in some example embodiments, are provided as partof application server(s) 118 in the networked system.

FIG. 3 is a network diagram illustrating a network environment suitablefor analysis of fashion preferences, according to some exampleembodiments.

FIG. 4 is a diagram illustrating an example of input data, according tosome example embodiments.

FIG. 5 is a functional diagram of an example preference analysismachine, according to some example embodiments.

FIG. 6 is a diagram illustrating example images depicting a user atdifferent points in time and corresponding example models representingthe body of the user at the different points in time, according to someexample embodiments.

FIG. 7 is a block diagram illustrating components of the preferenceanalysis machine, according to some example embodiments.

FIGS. 8-9 are flowcharts illustrating operations of the preferenceanalysis machine in performing a method of determining fashionpreferences of a user, according to some example embodiments.

FIG. 10 is a diagram illustrating examples of model clusters, accordingto some example embodiments.

FIG. 11 is a flowchart illustrating operations of the preferenceanalysis machine in performing a method of determining fashionpreferences of a user, according to some example embodiments.

FIG. 12 is a diagram illustrating example images and correspondingexample groups of image swatches extracted from the respective exampleimages, according to some example embodiments.

FIGS. 13-15 are flowcharts illustrating operations of the preferenceanalysis machine in performing a method of determining fashionpreferences of a user, according to some example embodiments.

FIG. 16 is a diagram illustrating example query images and correspondingexample search results, according to some example embodiments.

FIG. 17 is a block diagram illustrating a mobile device, according tosome example embodiments.

FIG. 18 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium and perform any one or more of the methodologiesdiscussed herein.

DETAILED DESCRIPTION

Example methods and systems for facilitating the determining of fashionpreferences of users and for providing item recommendations to the usersbased on the fashion preferences of the users are described. Examplesmerely typify possible variations. Unless explicitly stated otherwise,components and functions are optional and may be combined or subdivided,and operations may vary in sequence or be combined or subdivided. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth to provide a thorough understanding of exampleembodiments. It will be evident to one skilled in the art, however, thatthe present subject matter may be practiced without these specificdetails.

Since its inception, online shopping has proven to be an effectivemechanism to provide users with a greater selection of merchandise, atcompetitive prices. As more commerce entities obtain an online presence,the volume of merchandise available online increases dramatically. As aresult, shoppers may find it more difficult to quickly search andidentify items that meet their search criteria.

Generally, when initiating an online search for an item using a searchengine, a user enters a text query into a user interface. Users may facea couple of major pain points while searching for fashion items withtext. First, because of the difficulties in understanding the semanticaspect of long sentence queries, the use of long query phrases (e.g.,“blue striped half sleeve shirt with low cut collar”) may result in poorperformance of many state of the art text search engines. Second,fashion is predominantly concerned with visual aesthetics that are oftenbypassed by text search engines. Recent attempts have been made tofacilitate searches for items using image queries. However, suchattempts often do not result in better, more relevant search results. Animage search alone that is not customized to account for the fashiontastes, shapes (or body structures or body dimensions), preferredcolors, patterns, styles, or brand of the users may produce numeroussearch results that are irrelevant to the user.

Recent developments in virtual fitting rooms have addressed some aspectsof determining the body structure a user. However, many virtual fittingroom systems may not attempt to understand user style or fashionpreferences. Further, many virtual fitting room systems may not besuitable for studying natural fashion styles of users since the trips tovirtual fitting rooms are usually artificial, one-time occurrences thatmay not allow for the continuous observation of users' fashion choicesover extended periods of time.

In some example embodiments, a preference analysis system may determinethe fashion preferences of a user and provide item recommendations basedon the fashion preferences of the user. The preference analysis systemmay, according to certain example embodiments, collect visual andspatial data descriptive of the user in the user's natural environment(e.g., a living room or an office space) over a period time, maydetermine one or more fashion preferences of the user based on thecollected visual and spatial data, may identify one or more fashionitems (e.g., apparel, footwear, or accessories) that conform to theuser's fashion preferences, and may recommend the identified one or morefashion items to the user. By facilitating, in some example embodiments,implicit interactions by the user with the preference analysis systemwithout requesting the user to stand in certain predetermined positionswith respect to a device (e.g., a camera or a sensor) that collects thevisual and spatial data, the preference analysis system may provide amore user-friendly and time-saving mechanism of learning about theuser's fashion tastes and may provide relevant recommendations offashion items that are similar to or may be coordinated with thewardrobe items of the user.

In some example embodiments, the visual and spatial data is collectedusing imaging technology, such as that implemented in red, green, blue,and depth (hereinafter, “RGB-D”) sensors. Examples of products that usedepth sensing technology are the Microsoft™ XBOX 360® and XBOX One.Typical RGB images are obtained by projecting a three-dimensional(hereinafter, “3D”) scene onto a two-dimensional (hereinafter, “2D”)sensor, thus losing all information about depth. Because the RGB-Dsensors provide depth data, new technology advancements are possible inthe fields of motion capture and human body modeling. Furthermore,because the RGB-D data may be captured as video streams in which theuser exhibits complex motions, the preference analysis system may obtaindetailed information about apparel being worn on different parts of thebody and about the body of the user (e.g., layout, shape, ormeasurements).

According to some example embodiments, the dataset used by thepreference analysis system to determine the user's fashion preferenceshas three main sources of information in the form of a 3-tuple: the RGBchannel, the depth channel, and a human stick figure (e.g., generatedbased on the data captured by the depth sensor). The RGB-D dataset maybe denoted by {I_(n),D_(n),S_(n)}_(n=0) ^(N). A query image, that insome instances is used to perform a search of a fashion inventory basedon the user's fashion preferences, may be denoted by{I_(q),D_(q),S_(q)}. The triple variables stand for the RGB image, Depthimage and Skeleton array (e.g., identifiers of skeleton joints)respectively. Humans in RGB-D images can be represented using a skeletonmade up of a fixed number of bone joints, for example, P joints. Inother words, S_(n)={x_(1n), x_(2n) . . . x_(Pn)} comprises Pthree-dimensional coordinates corresponding to the location of each bonejoint. It may be further assumed that there are a set of R items ofapparel or accessories (e.g., a hat, sunglasses, a t-shirt, a watch, ashoe, an item of jewelry, a bag, etc.) denoted by {A_(r)}_(r=1) ^(R).

There is a variety of useful information that may be determined based onobserving the user in motion. Examples of such information are the bodydimensions and clothing styles of the user. The determining of theuser's body measurements may help customize the search for correctlysized clothing for the particular user. Generally, many users hesitateto discuss their body sizes or provide body dimension information toanyone (e.g., e-commerce operators). Users also may find it hard tocontinually keep track of their body measurements over time. Thepreference analysis system may addresses this issue by determining theuser's body measurements without asking explicit questions, and withoutissuing instructions to pose for measurements.

Advances in imaging sensors and algorithms may, in some exampleembodiments, allow the preference analysis system to determine precisebody dimensions for a number of body parts. Denoting the 2D image pixelcoordinates of a point by x={x, y} and its corresponding location in the3D world by X=[X, Y, Z], the two representations may be related by theprojection matrix, x=PX. Based on image pixel coordinates, thepreference analysis system may recover X, Y accurately using theprojection matrix, while Z may be recovered up to a scale factor.However, the depth channel of the depth (e.g., Kinect) sensor mayprovide the preference analysis system with the explicit depth valuesresolving the ambiguity in Z. Once the preference analysis system hasaccess to the 3D coordinates of the body joints of the user, thepreference analysis system may measure the user's body dimensions. Usingthe primitive of joint distances d_(M) in 3D on the human body manifoldM,

jtDist3D(i,j)=d _(M)(X _(i) −X _(j)),1≤i,j≤P.  (1)

In some example embodiments, the preference analysis system may projectevery pixel on the person's body into a 3D space yielding a full 3Dmodel of the person after fitting a mesh to the 3D points. In certainexample embodiments, the preference analysis system may generate themodel of the person based on the joint locations in the 3D space. Thebody measurements estimated from a flat 2D image lack a sense of depth.The depth sensors may help the preference analysis system to overcomethis issue by explicitly offering a depth channel for every pixel in thecolor image. Since the 3D world has been projected onto a 2D imagingsensor, the projection process may be reversed to recover the true 3Dcoordinates, using

${P = \begin{pmatrix}f_{x} & 0 & s_{x} \\0 & f_{y} & s_{y} \\0 & 0 & 1\end{pmatrix}},$

where (f_(x),f_(y)), (s_(x),s_(y)) are x−y camera focal lengths andprincipal offsets.

Once the body locations corresponding to different body joints have beenlocalized using {S_(n)}hd n=0 ^(N), in some example embodiments, thenext step may be to extract pixels for every body part on which apparelor other fashion items could be worn. In some instances, a number ofbody parts may be localized: the face (e.g., where glasses may be worn),the torso (e.g., where shirt, tops, t-shirts, or blazers may be worn),right/left hand (e.g., where watches or bracelets may be worn), theright or the left leg (e.g., where trousers, shorts or skirts may beworn), or the right or the left foot (e.g., where boots, shoes, orsandals may be worn). These body parts of interest, are inherentlycomposed of body part joints, as illustrated in FIG. 6 below. Forinstance, the torso body part B_(torso)={1,2} is made up of two jointdetected on the chest x_(2i) and bellybutton x_(1i). Examplerelationships between certain body parts and their constituent jointsare illustrated in Table 1, shown below.

TABLE 1 Tabulation of the relationships between the body parts and theirconstituent joints as returned by a pose estimation API in the Kinectsensor. Body Part Constituent Joints Face Forehead, Chin Torso Chest,Bellybutton Lower Hand Wrist, Inner Hand Upper Hand Shoulder, Elbow LegHip, Knee Foot Ankle, Feet

In some example embodiments, an image patch (e.g., swatch or region)that corresponds to a portion of a particular body part (e.g., thetorso) of the representation of the user within the image may beextracted from the image. If it is assumed that a certain body part ismade up on L joints, then the center of an image swatch and the size ofthe image swatch may be calculated using the following formulas:

$\begin{matrix}{{{Swatch}\mspace{14mu} {Center}} = {\frac{\sum_{i \in B_{torso}}x_{in}}{B_{torso}}\mspace{14mu} {and}}} & \; \\{{{Swatch}\mspace{14mu} {Dimension}} = {1.5 \times {\max_{i,j}{{{x_{in} - x_{jn}}}_{2}.}}}} & (2)\end{matrix}$

In other words, according to some example embodiments, the procedure forextracting an image swatch from the image of the user includes croppinga square box of dimension “Swatch Dimension”, centered at the point“Swatch Center”.

Once the regions of interest corresponding to different body parts havebeen cropped out from the image, they may be described using a suitablevisual descriptor. A visual descriptor may be the color descriptor in“Hues, Saturation, and Value” (also “HSV”) color space. The pixelsbelonging to each body part are represented by a set of real numbersthat are representative of the color contained in the cropped patch. Thepreference analysis system may utilize a non-uniform binning strategythat creates 24 bins for the H channel, 8 bins for the S channel, and 8bins for the V channel. In addition to color features, the preferenceanalysis system may also employ a gray scale histogram comprised of 8bins leading to a visual descriptor made up of 48 real numbers. Thevisual descriptor associated with the i^(th) body part in the j^(th)image may be denoted by the swatch v_(ij). In some example embodiments,in addition to or instead of a color-based descriptor, the preferenceanalysis system utilizes one or more other types of descriptors (e.g., atexture-based descriptor, a shape-based descriptor, etc.).

In some instances, the process of determining user fashion preferencesand of identifying fashion items compatible with the user's fashionpreferences includes estimating the compatibility between a certain poseand a fashion (e.g., apparel) item.

TABLE 2 Matrix I captures the co-occurrence between poses and fashionitems. Here, nine fashion items are denoted by A1 to A9 and seven posesare denoted by P1 to P7. A1 A2 A3 A4 A5 A6 A7 A8 A9 P1 1 P2 1 1 1 P3 1 11 P4 1 1 P5 1 1 P6 1 1 1 P7 1 1

The matrix I, illustrated in Table 2 above, is a boolean matrix (e.g.,every entry is either 1 or 0). The rows (i) correspond to differentpostures and columns (j) to different apparel (or other fashion) items.If a certain apparel item j can be reliably extracted from a posture i,then I(i,j)=1. Alternatively, if a certain apparel item j cannot bereliably extracted from a posture i, then I(i,j)=0. The posturesestimated by the depth (e.g., Kinect) sensor may have great variabilitybetween them. An example is the contrast between a user facing thecamera with hands up in the air, and a user facing the camera through aside view. In order for the preference analysis system to understand(e.g., determine) the posture a person is currently situated in, thepreference analysis system may employ pose clustering. To this end, thepreference analysis system may create a pose descriptor from a humanskeleton figure shown in FIG. 6. If the pose descriptor for the i^(t) hsubject is denoted by Pn, then the pose descriptor is the angle betweenjoint locations

P _(n)(j,k)=∠x _(jn) ,x _(kn).  (3)

Since the matrix Pn is symmetric, the upper triangular portion of thematrix may be vectorized to form the pose descriptor. In other words,every pose descriptor is a set of

$\frac{P\left( {P - 1} \right)}{2}$

real numbers concatenated together. These descriptors are part of afeature space describing postures users could be in. A clusteringprocedure may allow the grouping of similar poses together and thedifferentiating of the similar poses from other, dissimilar poses. Insome example embodiments, the preference analysis system groups clusteritems (e.g., items that may form a cluster) into a particular clusterbased on one or more clustering rules. For example, a first clusteringrule specifies that a cluster may be formed based on cluster items ofthe same type. In certain example embodiments, as a result of applyingthe first clustering rule, the preference analysis system may generate afirst cluster that groups images of one or more users, a second clusterthat groups models (e.g., stick figures) of one or more users, and athird cluster that groups combinations of an image and a model.

In another example, a second clustering rule specifies that clusteritems may be grouped together into a particular cluster based on thecluster items depicting similar poses of the user(s). The grouping ofcluster items into particular clusters based on a particular pose (orsimilar poses) may facilitate the identifying of areas (e.g., in theimages or models) that correspond to unobstructed body parts of the userdepicted in the images or models. The grouping of cluster items intoparticular clusters based on a particular pose (or similar poses) mayfurther facilitate the performing of a more accurate analysis of theareas in the images or models that correspond to the unobstructed bodyparts. One or more cluster items included in a cluster may be used toanalyze particular areas that correspond to the unobstructed body partsof the user depicted in the cluster items included in the cluster.

According to one example, the clustering procedure groups all imagesillustrating postures of a user facing the camera with the user's armsstretched upwards, into a first cluster. According to another example,the images of postures of the user having the user's hands in front ofthe user are grouped into a second cluster, distinct from the firstcluster. This scenario is shown in FIG. 11.

The clustering procedure can be described using the following formula:

$\begin{matrix}{{\arg \mspace{11mu} {\min\limits_{S}{\overset{K}{\sum\limits_{i = 1}}{\sum\limits_{P_{j} \in S_{i}}{{P_{j} - \mu_{i}}}^{2}}}}},} & (4)\end{matrix}$

where K is the number of pose clusters to be analyzed, and S_(i), 1≤i≤Kis the set of clusters, each containing a subset or partitioning of theentire space of postures users are present in. The pose clustering mayreduce a large number of data points (equal to the number of videoframes captures) to a compact set of exemplar posture signatures μ_(I),1≤i≤K, where each signature is a representative example from eachcluster. Examples of postures grouped together in clusters are imagesthat depict a frontal view of the user, images that depict a side viewof the user, images that depict the user having the user's arms raisedup, images that depict a hand in front of the user's torso, or imagesthat depict a sitting user.

The pose clustering may be used because the quality of extracted imageswatches may depend on the particular posture of the user. If aparticular body part of the user is located further away from the camerain relation to other body parts of the user, or is obstructed by anotherbody part or another object in the room, an image swatch of theparticular body part may not include sufficient information fordetermining a user preference with respect to items worn on theparticular body part. For instance, if the user wears a watch on theuser's left wrist and the user's right side of the body faces thecamera, attempting to extract an image swatch depicting the user's watchon his left wrist may not result in a good quality image swatch.Similarly, an attempt to extract information about the user's shirt whenthe user has the user's arms folded in front of the user's torso may notbe successful because the preference analysis system may erroneouslysample pixels belonging to the representation of the user's arm or handwithin the image instead of the pixels belonging to the representationof the shirt.

In certain example embodiments, the preference analysis system evaluatesthe accuracy of the extracted swatches. The accuracy of an extractedimage swatch may be assessed based on how well the extracted imageswatch represents (e.g., depicts or illustrates) the body part ofinterest. As part of the process of extracting an image swatch, thepreference analysis system superimposes a bounding box on an image at alocation (e.g., an area or a region) within the image that representsthe body part of interest. If the bounding box frames a portion of therepresentation of the body part of interest, a high score may beassociated with the image swatch (e.g., as metadata of the imageswatch). If the bounding box fails to enclose a portion of therepresentation of the body part, a low score is associated with theimage swatch. In some instances, only highly reliable swatches arestored in a record of a database and are used for further analyses.

In some example embodiments, the preference analysis system tailors asearch of an inventory of fashion items based on user preferences forparticular fashion items (or characteristics of fashion items, such asstyle, color or pattern) and the user's measurements. A focused searchedcustomized for a particular user may eliminate frictions within ashopping process, provide relevant recommendations of fashion items, andincrease sales of the recommended items.

Observing user activity may help in customizing searches to thepreferences of a specific user. In some example embodiments, once theimage swatches are extracted from one or more images of the observeduser, the preference analysis system may perform a search of fashionitems within an inventory of fashion items to generate search resultssuitable for recommending to the user, based on the user's measurementsand fashion preferences. In some instances, the searching of theinventory of fashion items includes comparing a query swatch (e.g., animage swatch extracted from a received image of the user) with adifferent image that depicts a fashion item included in the inventory offashion items.

In some example embodiments, the preference analysis system performs asimilarity search of an inventory of fashion items based on theextracted image swatch. The performing of the similarity search mayinclude identifying, within the fashion inventory, a fashion item thatmatches the extracted image swatch as closely as possible, for example,based on matching visual features in the image swatch extracted from thereceived image and visual features in a different image that depicts anfashion item in the inventory of fashion items.

According to certain example embodiments, given a query swatch extractedfrom the image of the user, v_(ij) ^(query), the preference analysissystem searches for a visually similar item in the inventory of fashionitems (e.g., the fashion items for sale on an e-commerce sites). It maybe assumed that the inventory is made up of U={(U^(glass), U^(shoes),U^(shirt), . . . , U^(trouser)} The inventory dataset may be comprisedof visual features corresponding to various fashion items, such asglasses, shirts, shoes, trousers, etc. In some instances, a result of asimilarity search may be determined based on a nearest neighbor matchand may include one or more items most similar to the query swatch. Forexample, an inventory item may be identified as being most similar tothe query swatch based on the inventory item having a number (or apercentage) of visual features in common with the query swatch. In someinstances, the inventory item is identified as being most similar to thequery swatch based on the inventory item having a number (or apercentage) of visual features in common with the query swatch thatexceeds a particular threshold value.

In some example embodiments, the preference analysis system performs acoordination search of an inventory of fashion items based on theextracted image swatch. The performing of the coordination search mayinclude identifying, within the inventory of fashion items, a fashionitem that may be worn together with an item previously worn by the user,based on a coordination rule. In some instances, one or morecoordination rules describing one or more fashion preferences of theuser may be generated based on analyzing the fashion choices previouslymade by the user (e.g., items previously worn together, items previouslypurchased by the user, following certain recommendations of fashionitems, etc.). In some example embodiments, in addition to or instead ofapplying one or more coordination rules describing one or more fashionpreferences of the user to perform the coordination search, thepreference analysis system utilizes one or more coordination rulesdetermined by fashion experts, or one or more coordination rulesmachine-learnt from data that describes fashion preferences (e.g.,fashion preference patterns) of a plurality of people (e.g., buyers,designers, fashion bloggers, fashion experts, fashion models, peopleexhibiting street styles, etc.).

According to certain example embodiments, given a query apparel (e.g., ashirt query) and a target apparel item (e.g., trousers that go well withthe shirt), a coordination transformation may be first determinedbetween query and target spaces denoted by T_(query→target). In such ascenario, once a query swatch with visual signature v_(ij) ^(query) isextracted (e.g., from the image of the query apparel), the preferenceanalysis system may transform the query into target space (e.g.,color/pattern space for trousers). This transformation may be performedbased on the following formula:

v _(ij) ^(target) =T _(query→target) v _(ij) ^(query).  (5)

Once the target visual signature is computed, the preference analysissystem may perform a similarity search on the target space based on avisual search described above. It may be useful to note that, given ashirt query, a visual search during a similarity search may be performedover the query space such that similar shirt items are identified assearch results. In contrast, during a coordination search, thepreference analysis system may first transform the visual signature fromquery space to a target space (e.g., trousers) and then perform thefinal search over the target space.

In some example embodiments, upon performing a similarity search or acoordination search, the preference analysis system generates arecommendation that includes one or more results of the similaritysearch or of the coordination search. The recommendation may betransmitted in a communication to the user.

According to certain example embodiments, the preference analysis systemmay analyze a user's fashion preferences and may estimate dimensions ofthe user's body parts regardless of the user's pose or viewpoint fromthe camera. The capturing of the user's image and spatial data thatdescribes the body and pose of the user within the 3D space may takeplace within the user's home, office environment, or even a publicspace.

For example, a large public display is a popular medium for interactingwith shoppers in malls and other public locations. Large displaysinvariably invoke curiosity among users, and sustaining user involvementover longer periods of time may be important to the success of thesedisplays. Such a display may include a camera (of the preferenceanalysis system) to capture user images and spatial data describing theuser's body and pose(s). Once a user walks by such a display, the cameramay capture the images of the user and the spatial data and thepreference analysis system may determine the user's body dimensions,characteristics of the user's apparel (e.g., a clothing style), or both,based on the user's images and the spatial data describing the user'sbody and pose(s). Based on the user's body dimensions, characteristicsof the user's apparel, or both, the preference analysis system may beginmaking recommendations to the user right away.

Moreover, in some instances, the recommendations may be augmented toimpress the user. According to one example, if the search for fashionitems to be recommended to the user is being performed in the month ofDecember, Christmas outfits that suit the user may be recommended to theuser while the user is located in front of the public display. If thesearch query is being performed during a sporting event (e.g., the SuperBowl), the preference analysis system may recommend clothingrepresenting the competing teams. According to another example, thepreference analysis system may recommend fashion items to a personplaying a video game, in accordance with one or more themes (orcharacters) of the video game. According to yet another example, thepreference analysis system may recommend clothing to a user watching atelevision show, according to the particular show being watched,characters appearing in the show, or actors starring in the show. Forinstance, the preference analysis system may build computational modelsof clothing styles based on currently popular celebrities. Thepreference analysis system may recommend fashion items based on the showbeing played and the computational models of clothing styles of thecelebrities appearing in the particular show.

FIG. 1 is a network diagram depicting a client-server system 100, withinwhich one example embodiment may be deployed. A networked system 102, inthe example forms of a network-based marketplace or publication system,provides server-side functionality, via a network 104 (e.g., theInternet or a Wide Area Network (WAN)), to one or more clients. FIG. 1illustrates, for example, a web client 106 (e.g., a browser, such as theInternet Explorer browser developed by Microsoft Corporation of Redmond,Wash. State) and a programmatic client 108 executing on respectivedevices 110 and 112.

An Application Program Interface (API) server 114 and a web server 116are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 118. The application servers 118host one or more marketplace applications 120 and payment applications122. The application servers 118 are, in turn, shown to be coupled toone or more database servers 124 that facilitate access to one or moredatabases 126.

The marketplace applications 120 may provide a number of marketplacefunctions and services to users who access the networked system 102. Invarious example embodiments, the marketplace applications 120 mayinclude a preference analyzer 132. The preference analyzer 132, in someexample embodiments, may determine the fashion preferences of users andmay generate item recommendations based on the users' fashionpreferences.

The payment applications 122 may likewise provide a number of paymentservices and functions to users. The payment applications 122 may allowusers to accumulate value (e.g., in a commercial currency, such as theU.S. dollar, or a proprietary currency, such as “points”) in accounts,and then later to redeem the accumulated value for products (e.g., goodsor services) that are made available via the marketplace applications120. While the marketplace and payment applications 120 and 122 areshown in FIG. 1 to both form part of the networked system 102, it willbe appreciated that, in alternative embodiments, the paymentapplications 122 may form part of a payment service that is separate anddistinct from the networked system 102.

Further, while the system 100 shown in FIG. 1 employs a client-serverarchitecture, the embodiments are, of course, not limited to such anarchitecture, and could equally well find application in a distributed,or peer-to-peer, architecture system, for example. The variousmarketplace and payment applications 120 and 122 could also beimplemented as standalone software programs, which do not necessarilyhave networking capabilities.

The web client 106 accesses the various marketplace and paymentapplications 120 and 122 via the web interface supported by the webserver 116. Similarly, the programmatic client 108 accesses the variousservices and functions provided by the marketplace and paymentapplications 120 and 122 via the programmatic interface provided by theAPI server 114. The programmatic client 108 may, for example, be aseller application (e.g., the TurboLister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 108and the networked system 102.

FIG. 1 also illustrates a third party application 128, executing on athird party server machine 130, as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 114. For example, the third party application 128 may, utilizinginformation retrieved from the networked system 102, support one or morefeatures or functions on a website hosted by the third party. The thirdparty website may, for example, provide one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

FIG. 2 is a block diagram illustrating marketplace and paymentapplications 120 and 122 that, in one example embodiment, are providedas part of application server(s) 118 in the networked system 102. Theapplications 120 and 122 may be hosted on dedicated or shared servermachines (not shown) that are communicatively coupled to enablecommunications between server machines. The applications 120 and 122themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources, so as to allowinformation to be passed between the applications 120 and 122 or so asto allow the applications 120 and 122 to share and access common data.The applications 120 and 122 may furthermore access one or moredatabases 126 via the database servers 124.

The networked system 102 may provide a number of publishing, listing,and price-setting mechanisms whereby a seller may list (or publishinformation concerning) goods or services for sale, a buyer can expressinterest in or indicate a desire to purchase such goods or services, anda price can be set for a transaction pertaining to the goods orservices. To this end, the marketplace and payment applications 120 and122 are shown to include at least one publication application 200 andone or more auction applications 202, which support auction-formatlisting and price setting mechanisms (e.g., English, Dutch, Vickrey,Chinese, Double, Reverse auctions, etc.). The various auctionapplications 202 may also provide a number of features in support ofsuch auction-format listings, such as a reserve price feature whereby aseller may specify a reserve price in connection with a listing and aproxy-bidding feature whereby a bidder may invoke automated proxybidding.

A number of fixed-price applications 204 support fixed-price listingformats (e.g., the traditional classified advertisement-type listing ora catalogue listing) and buyout-type listings. Specifically, buyout-typelistings (e.g., including the Buy-It-Now (BIN) technology developed byeBay Inc., of San Jose, Calif.) may be offered in conjunction withauction-format listings, and allow a buyer to purchase goods orservices, which are also being offered for sale via an auction, for afixed-price that is typically higher than the starting price of theauction.

Store applications 206 allow a seller to group listings within a“virtual” store, which may be branded and otherwise personalized by andfor the seller. Such a virtual store may also offer promotions,incentives, and features that are specific and personalized to arelevant seller.

Reputation applications 208 allow users who transact, utilizing thenetworked system 102, to establish, build, and maintain reputations,which may be made available and published to potential trading partners.Consider that where, for example, the networked system 102 supportsperson-to-person trading, users may otherwise have no history or otherreference information whereby the trustworthiness and credibility ofpotential trading partners may be assessed. The reputation applications208 allow a user (for example, through feedback provided by othertransaction partners) to establish a reputation within the networkedsystem 102 over time. Other potential trading partners may thenreference such a reputation for the purposes of assessing credibilityand trustworthiness.

Personalization applications 210 allow users of the networked system 102to personalize various aspects of their interactions with the networkedsystem 102. For example a user may, utilizing an appropriatepersonalization application 210, create a personalized reference page atwhich information regarding transactions to which the user is (or hasbeen) a party may be viewed. Further, a personalization application 210may enable a user to personalize listings and other aspects of theirinteractions with the networked system 102 and other parties.

The networked system 102 may support a number of marketplaces that arecustomized, for example, for specific geographic regions. A version ofthe networked system 102 may be customized for the United Kingdom,whereas another version of the networked system 102 may be customizedfor the United States. Each of these versions may operate as anindependent marketplace or may be customized (or internationalized)presentations of a common underlying marketplace. The networked system102 may accordingly include a number of internationalizationapplications 212 that customize information (and/or the presentation ofinformation by the networked system 102) according to predeterminedcriteria (e.g., geographic, demographic or marketplace criteria). Forexample, the internationalization applications 212 may be used tosupport the customization of information for a number of regionalwebsites that are operated by the networked system 102 and that areaccessible via respective web servers 116.

Navigation of the networked system 102 may be facilitated by one or morenavigation applications 214. For example, a search application (as anexample of a navigation application 214) may enable key word searches oflistings published via the networked system 102. A browse applicationmay allow users to browse various category, catalogue, or inventory datastructures according to which listings may be classified within thenetworked system 102. Various other navigation applications 214 may beprovided to supplement the search and browsing applications.

In order to make listings available via the networked system 102 asvisually informing and attractive as possible, the applications 120 and122 may include one or more imaging applications 216, which users mayutilize to upload images for inclusion within listings. An imagingapplication 216 also operates to incorporate images within viewedlistings. The imaging applications 216 may also support one or morepromotional features, such as image galleries that are presented topotential buyers. For example, sellers may pay an additional fee to havean image included within a gallery of images for promoted items.

Listing creation applications 218 allow sellers to conveniently authorlistings pertaining to goods or services that they wish to transact viathe networked system 102, and listing management applications 220 allowsellers to manage such listings. Specifically, where a particular sellerhas authored and/or published a large number of listings, the managementof such listings may present a challenge. The listing managementapplications 220 provide a number of features (e.g., auto-relisting,inventory level monitors, etc.) to assist the seller in managing suchlistings. One or more post-listing management applications 222 alsoassist sellers with a number of activities that typically occurpost-listing. For example, upon completion of an auction facilitated byone or more auction applications 202, a seller may wish to leavefeedback regarding a particular buyer. To this end, a post-listingmanagement application 222 may provide an interface to one or morereputation applications 208, so as to allow the seller conveniently toprovide feedback regarding multiple buyers to the reputationapplications 208.

Dispute resolution applications 224 provide mechanisms whereby disputesarising between transacting parties may be resolved. For example, thedispute resolution applications 224 may provide guided procedureswhereby the parties are guided through a number of steps in an attemptto settle a dispute. In the event that the dispute cannot be settled viathe guided procedures, the dispute may be escalated to a third partymediator or arbitrator.

A number of fraud prevention applications 226 implement fraud detectionand prevention mechanisms to reduce the occurrence of fraud within thenetworked system 102.

Messaging applications 228 are responsible for the generation anddelivery of messages to users of the networked system 102 (such as, forexample, messages advising users regarding the status of listings at thenetworked system 102 (e.g., providing “outbid” notices to bidders duringan auction process or to provide promotional and merchandisinginformation to users)). Respective messaging applications 228 mayutilize any one of a number of message delivery networks and platformsto deliver messages to users. For example, messaging applications 228may deliver electronic mail (e-mail), instant message (IM), ShortMessage Service (SMS), text, facsimile, or voice (e.g., Voice over IP(VoIP)) messages via the wired (e.g., the Internet), plain old telephoneservice (POTS), or wireless (e.g., mobile, cellular, WiFi, WiMAX)networks 104.

Merchandising applications 230 support various merchandising functionsthat are made available to sellers to enable sellers to increase salesvia the networked system 102. The merchandising applications 230 alsooperate the various merchandising features that may be invoked bysellers, and may monitor and track the success of merchandisingstrategies employed by sellers.

The networked system 102 itself, or one or more parties that transactvia the networked system 102, may operate loyalty programs that aresupported by one or more loyalty/promotions applications 232. Forexample, a buyer may earn loyalty or promotion points for eachtransaction established and/or concluded with a particular seller, andbe offered a reward for which accumulated loyalty points can beredeemed.

FIG. 3 is a network diagram illustrating a network environment 300suitable for analysis of user fashion preferences, according to someexample embodiments. The network environment 300 includes a preferenceanalysis machine 310 (e.g., the preference analyzer 132), a database126, and devices 330 and 350, all communicatively coupled to each othervia a network 390. The preference analysis machine 310, with or withoutthe database 126, may form all or part of a network-based system 305(e.g., a cloud-based server system configured to provide one or moreimage processing services to the devices 330 and 350). One or both ofthe devices 330 and 350 may include a camera that allows capture of animage (e.g., an image of a user in a living room) or of depth (orspatial) data descriptive of the environment external to the camera, orboth. One or both of the devices 330 and 350 may facilitate thecommunication of the image, the spatial data, or both, (e.g., as asubmission to the database 126) to the preference analysis machine 310.The preference analysis machine 310 and the devices 330 and 350 may eachbe implemented in a computer system, in whole or in part, as describedbelow with respect to FIG. 18.

Also shown in FIG. 3 are users 332 and 352. One or both of the users 332and 352 may be a human user (e.g., a human being), a machine user (e.g.,a computer configured by a software program to interact with the device330), or any suitable combination thereof (e.g., a human assisted by amachine or a machine supervised by a human). The user 332 is not part ofthe network environment 300, but is associated with the device 330 andmay be a user of the device 330. For example, the device 330 may be adesktop computer, a vehicle computer, a tablet computer, a navigationaldevice, a portable media device, a smartphone, or a wearable device(e.g., a smart watch or smart glasses) belonging to the user 332.Likewise, the user 352 is not part of the network environment 300, butis associated with the device 350. As an example, the device 350 may bea desktop computer, a vehicle computer, a tablet computer, anavigational device, a portable media device, a smartphone, or awearable device (e.g., a smart watch or smart glasses) belonging to theuser 352.

Also shown in FIG. 3 are the depth sensors 334 and 354 (e.g., theMicrosoft™ Kinect™, hereinafter, “Kinect”, a mobile device, such as acell phone, tablet, or PDA; or a camera; hereinafter, also “depthsensor(s)”). The network environment 300 may include one or more depthsensors. In some example embodiments, the depth sensor 334 may be partof the device 330. In other example embodiments, the depth sensor 334may be external to the device 330. Similarly, the depth sensor 354 maybe part of the device 350. In other example embodiments, the depthsensor 354 may be external to the device 350.

Each of the depth sensors 334 and 354 may capture (e.g., receive,gather, or collect) spatial data about the physical space external tothe depth sensor (e.g., spatial data about user 332) and transmit thecaptured spatial data to the device 330 or the device 350, which in turnmay transmit some or all of the spatial data captured by the depthsensors 334 and 354 to the preference analysis machine 310 via network390. In some example embodiments, the depth sensors 334 and 354 maycommunicate with and send the captured spatial data to the preferenceanalysis machine 310 via network 390 without first sending the spatialdata to either of the devices 330 or 350.

In some example embodiments, the depth sensors 334 and 354 are absentfrom the network environment 300. The device 330 or the device 350 maycapture images of the user 332 or the user 352, respectively, and maytransmit the images to the preference analysis machine 310, via thenetwork 390, for determination of user fashion preferences based on thecaptured images. For example, if the depth channel is not present, thepreference analysis machine 310 may utilize the RGB channel data todetermine the fashion preferences of the user(s).

In some example embodiments, some or all of the functionality of thepreference analysis machine 310 is performed by the device 330 or thedevice 350. For example, an application hosted on the device 330 (e.g.,an app stored on a smartphone) may perform an analysis of the image dataand/or spatial data received from the camera of the device 330 or thedepth sensor 334. The received image data and/or received spatial datamay describe the items worn by the user 332 and the body of the user332. The application hosted on the device 330 may, in some instances,determine one or more fashion preferences of the user 332. In someinstances, the application hosted on the device 330 may transmit theresults of the analysis of the image data and/or spatial data to thepreference analysis machine 310 for determination of fashion preferencesof the user 332 and/or for a search of an inventory of fashion itemsbased on the fashion preferences of the user 332.

Any of the machines, databases, or devices shown in FIG. 3 may beimplemented in a general-purpose computer modified (e.g., configured orprogrammed) by software (e.g., one or more software modules) to be aspecial-purpose computer to perform one or more of the functionsdescribed herein for that machine, database, or device. For example, acomputer system able to implement any one or more of the methodologiesdescribed herein is discussed below with respect to FIG. 18. As usedherein, a “database” is a data storage resource and may store datastructured as a text file, a table, a spreadsheet, a relational database(e.g., an object-relational database), a triple store, a hierarchicaldata store, or any suitable combination thereof. Moreover, any two ormore of the machines, databases, or devices illustrated in FIG. 3 may becombined into a single machine, and the functions described herein forany single machine, database, or device may be subdivided among multiplemachines, databases, or devices.

The network 390 may be any network that enables communication between oramong machines, databases, and devices (e.g., the server machine 310 andthe device 330). Accordingly, the network 390 may be a wired network, awireless network (e.g., a mobile or cellular network), or any suitablecombination thereof. The network 390 may include one or more portionsthat constitute a private network, a public network (e.g., theInternet), or any suitable combination thereof. Accordingly, the network390 may include one or more portions that incorporate a local areanetwork (LAN), a wide area network (WAN), the Internet, a mobiletelephone network (e.g., a cellular network), a wired telephone network(e.g., a plain old telephone system (POTS) network), a wireless datanetwork (e.g., WiFi network or WiMax network), or any suitablecombination thereof. Any one or more portions of the network 390 maycommunicate information via a transmission medium. As used herein,“transmission medium” refers to any intangible (e.g., transitory) mediumthat is capable of communicating (e.g., transmitting) instructions forexecution by a machine (e.g., by one or more processors of such amachine), and includes digital or analog communication signals or otherintangible media to facilitate communication of such software.

FIG. 4 is a diagram illustrating an example of input data, according tosome example embodiments. The preference analysis machine 310, usingskeletal and depth tracking technology implemented in a depth sensor,may gather spatial data that describes objects located in the physicalenvironment external to the depth sensor (e.g., the user's living room).The skeletal and depth tracking technology may be implemented in a depthsensor (e.g., the Kinect), stereo cameras, mobile devices, and any otherdevice that may capture depth data. In some example embodiments, theskeletal and depth tracking technology is implemented on a server usingalgorithms that utilize the RGB and depth channels.

In some example embodiments, depth sensing technologies use structuredlight or time of flight based sensing. For example, an infrared(hereinafter, also “IR”) emitter that is part of the preference analysismachine 310 and that is located in the user's living room, may project(e.g., emit or spray out) beams of infrared light into surroundingspace. The projected beams of IR light may hit and reflect off objectsthat are located in their path (e.g., the user or a physical object inthe user's living room). A depth sensor (e.g., located in the user'sliving room) may capture (e.g., receive) spatial data about thesurroundings of the depth sensor based on the reflected beams of IRlight. In some example embodiments, the captured spatial data may beused to create (e.g., represent, model, or define) a 3D field of viewthat may be displayed on a screen (e.g., of a TV set, computer, ormobile device). Examples of such spatial data include the location andshape of the objects within the room where the spatial sensor islocated.

In some example embodiments, based on measuring how long it takes thebeams of IR light to reflect off objects they encounter in their pathand be captured by the depth sensor, the preference analysis machine 310may determine the location (e.g., the distance from the depth sensor) ofthe objects off which the beams of IR light reflected (e.g., the user, afurniture piece, or a wall). In various example embodiments, based onthe received spatial data, the system may determine details of theobjects in the room, such as spatial measurements of the objects in theroom (e.g., the dimensions of the user's body).

In some example embodiments, the images of the user 332 (e.g., capturedby a camera that is included in the device 330) and spatial datacollected via the depth sensor 334 over a period of time serve as inputdata to the preference analysis machine 310. FIG. 4 illustrates anexample image 410 (e.g., an RGB image) of the user 332 and examplespatial data 420 (e.g., a jet pseudo-colored depth image) that isindicative of the physical environment external to the depth sensor 334.The spatial data 420 includes information about the shape of the body ofthe user 332, the dimensions of the body of the user 332, the distancebetween the depth sensor 334 and the user 332, etc. According to certainexample embodiments, the preference analysis machine 310 may analyze theinput data to determine the user's fashion preferences. The preferenceanalysis machine 310 may also identify fashion items available forpurchase that match the user's fashion preferences and the user's bodymeasurements, and may recommend the identified fashion items to theuser.

FIG. 5 is a functional diagram of an example preference analysis machine310, according to some example embodiments. In some example embodiments,the preference analysis machine 310 is included in a network-basedsystem 500. As described in more detail below, the preference analysismachine 310 may receive an image and a set of spatial data 510 (e.g., animage of the user 332 and spatial data indicative of the body of theuser 332 in the space external to the depth sensor 334). The image andthe set of spatial data 510 may be received from the device 330associated with the user 332. The image and the set of spatial data 510may be received together at the same time or independently of each otherat different times, and from different data capturing devices. Incertain example embodiments, the device 330 includes the depth sensor334.

In response to receiving the image and spatial data 510, the preferenceanalysis machine 310 analyses (e.g., performs an image and spatial dataanalysis 520 of) the received image and the received set of spatial data510 (also “the image and the set of spatial data 510”) using at leastone computer processor. In some example embodiments, to perform theimage and spatial data analysis 520, the preference analysis machine 310generates a model representing the body of the user based on the set ofspatial data received at a particular time and determines, based on ananalysis of the image and the model, one or more attribute-value pairsthat characterize an item worn by the user on a particular body part ofthe user at the particular time. An attribute of an item worn by theuser may be a characteristic of the item, such as a style, color,pattern, brand, fabric, or texture. A value of an attribute is adescription that the attribute may take. Examples of attribute-valuepairs may be “style: casual”, “color: red”, or “material: leather.”

The preference analysis machine 310 may perform the image and spatialdata analysis 520 based on a plurality of images of the user and aplurality of sets of spatial data received at a plurality of times.Accordingly, the preference analysis machine 310 may generate aplurality of models representing the body of the user at the pluralityof times, and may determine, based on the analysis of the plurality ofimage and the plurality of models, one or more attribute-value pairsthat characterize each of the items worn by the user on the particularbody part at each of the plurality of times.

In certain example embodiments, the preference analysis machine 310identifies a fashion preference 530 of the user 332 based on a frequencyof the user wearing items characterized by a particular attribute-valuepair during a period of time. For example, the preference analysismachine 310 may determine how many days in a particular month the userwore shirts characterized by the attribute-value pair “style: casual.”In another example, the preference analysis machine 310 may determinehow many days in a particular month the user wore shoes characterized bythe attribute-value “material: leather.”

In some example embodiments, upon identifying the user preference 530 ofthe user 332, the preference analysis machine 310 performs a search 540of an inventory of items for sale (e.g., a plurality of listings ofitems for sale) based on the identified user preference 530. In someinstances, the preference analysis machine 310 may perform, based on theuser preference 530, a similarity search to identify fashion items thatare similar to the items frequently worn by the user 332. In otherinstances, the preference analysis machine 310 may perform, based on theuser preference 530, a coordination search to identify fashion itemsthat may be coordinated with (e.g., worn together with) one or moreitems worn by the user 332.

The preference analysis machine 310 may, in some example embodiments,generate a recommendation 550 of relevant search results that mayinclude one or more results of the similarity search, coordinationsearch, or both. The preference analysis machine 310 may then transmit acommunication that includes the recommendation 550 of relevant searchresults to the device 330 associated with the user 332.

FIG. 6 is a diagram illustrating example images depicting a user atdifferent points in time and corresponding example models representingthe body of the user at the different points in time, according to someexample embodiments. The images 610, 630, 650, and 670 shown in FIG. 6represent a plurality of images of the user 332 that are captured by thedevice 330 at certain times during a period of time. For example, theimages 610, 630, 650, and 670 may be frames from a Kinect videosequence. The images 610, 630, 650, and 670 illustrate the user 332performing a number of activities (e.g., standing, walking, sitting,exercising, or stretching).

Also shown in FIG. 6 are examples of models 620, 640, 660, and 680 thatrepresent the body of the user 332 in a particular pose corresponding tothe images 610, 630, 650, and 670, respectively. Each of the poses seenin the models 620, 640, 660, and 680 reflect a position of the body ofthe user 332, as illustrated in each of the corresponding images 610,630, 650, and 670.

In some example embodiments, a model of the body of the user 332 maytake the form of a stick figure, as shown in the models 620, 640, 660,and 680 depicted in FIG. 6. The model of the body of the user 332 mayinclude identifiers of the locations of the bone joints of the body ofthe user 332. The preference analysis machine 310 may identify certainbody parts of the user 332 in the model based on the identifiers of thelocations of the bone joints of the body of the user 332. Based on theidentified body parts in the model, the preference analysis machine 310may determine user preferences for fashions worn on the respective bodyparts.

FIG. 7 is a block diagram illustrating components of the preferenceanalysis machine 310, according to some example embodiments. Thepreference analysis machine 310 is shown as including a receiver module710, a generation module 720, an analysis module 730, a preferencemodule 740, a search module 750, a recommendation module 760, and acommunication module 770, all configured to communicate with each other(e.g., via a bus, shared memory, or a switch).

Any one or more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. Moreover,any two or more of these modules may be combined into a single module,and the functions described herein for a single module may be subdividedamong multiple modules. Furthermore, according to various exampleembodiments, modules described herein as being implemented within asingle machine, database, or device may be distributed across multiplemachines, databases, or devices.

FIGS. 8-10 are flowcharts illustrating operations of the preferenceanalysis machine 310 in performing a method 800 of determining fashionpreferences of a user and providing fashion item recommendations basedon the user's fashion preferences, according to some exampleembodiments. Operations in the method 800 may be performed using modulesdescribed above with respect to FIG. 7. As shown in FIG. 8, the method800 may include one or more of operations 810, 820, and 830.

At method operation 810, the receiver module 710 accesses (e.g.,receives, obtains, or captures) an image of a user and a set of spatialdata indicating a position of the user in a three-dimensional space at aparticular time (e.g., a point in time). The received image (also “theimage”) and the received set of spatial data (also “the set of spatialdata”) may be captured at the particular time. In some exampleembodiments, the received image and the received set of spatial data areinput data received by the receiver module 710 from a device (e.g., theKinect) associated with the user. The image, the set of spatial data, orboth may be captured by a depth sensor which may communicate the image,the set of spatial data, or both to the device associated with the user.In some instances, the depth sensor may bypass the device associatedwith the user, and may transmit the image, the set of spatial data, orboth directly to the preference analysis machine 310 (e.g., to thereceiver module 710).

At method operation 820, the analysis module 730 performs, using one ormore hardware processors, an analysis of the received image and of thereceived set of spatial data. The performing of the analysis may includeextracting an image swatch from the received image. The image swatch maydepict a portion of an item worn by the user at the particular time. Theanalysis module 730 may store the image swatch in a record of a database(e.g., the database 126).

In some example embodiments, the analysis of the image and the modelincludes the analysis of an area of the image that corresponds to aparticular body part of the user and that depicts a fashion item worn bythe user at the particular time. In certain example embodiments, theanalysis module 730 determines one or more measurements (e.g.,dimensions) of the body of the user as part of the analysis of the imageand the model. The analysis module 730 may also determine, based on themeasurements of the user's body, one or more sizes of fashion items fromdifferent brands (e.g., manufacturers or sellers of fashion items) thatmay fit the user's body.

At method operation 830, the preference module 730 identifies a fashionpreference of the user based on the analysis of the received image andof the received set of spatial data. For example, based on the analysisof the received image and of the received set of spatial data, theanalysis module 730 may determine that the user likes to wear shirtsmade of blue denim fabric. Further details with respect to the methodoperations of the method 800 are described below with respect to FIGS.8A-9, 11, and 13-15.

As shown in FIG. 8A, the method 800 may include one or more ofoperations 811. Method operation 811 may be performed after methodoperation 810, in which the receiver module 710 receives the image ofthe user and the set of spatial data indicating the position of the userin a three-dimensional space at a particular time.

At method operation 811, the generation module 720 generates, using oneor more processors, a model representing the body of the user at theparticular time based on the set of spatial data. In some exampleembodiments, the model includes a stick figure representing a pose ofthe user depicted in the image captured at the particular time.

As shown in FIG. 9, the method 800 may include one or more of methodoperations 901, 902, and 903, according to some example embodiments.Method operation 901 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of method operation 820, in which the analysismodule 730 performs an analysis of the received image and of thereceived set of spatial data. At method operation 901, the analysismodule 730 analyzes the pose of the user that is depicted in the modelrepresenting the position of the body of the user at the particulartime.

Method operation 902 may be performed after method operation 901. Atmethod operation 902, the analysis module 730 identifies, based on theanalysis of the pose, an area (e.g., the area that represents the user'storso) of the model that represents a specific body part (e.g., thetorso) of the user that is unobstructed by an object (e.g., another bodypart of the user, an item of furniture, another person, an animal,etc.).

Method operation 903 may be performed after method operation 902. Atmethod operation 903, the analysis module 730 clusters (e.g., groups,generates a cluster of, or generates a group of) the model with one ormore other models representing the body of user at other times. Theclustering of the models may be based on the area of the model thatrepresents the specific body part of the user and the same area of theone or more other models being unobstructed by another body part orobject.

For example, the model is a stick figure representing the position ofthe user's body at a particular time. The analysis module 730 maycluster stick figures representing the body of the user at differenttimes. The clustering of the stick figures may be based on a similarityin poses of the user's body. For instance, the analysis module 730 mayidentify stick figures which have torso areas (representing the torso ofthe user) that are unobstructed by the user's arms or hands. Theanalysis module 730 may then group the identified stick figures into acluster. The cluster may map to (e.g., may be identified as illustratingor describing) a particular body part (e.g., the torso) that isunobstructed by another body part or object.

In some example embodiments, a cluster is a library of stick figuresthat are generated based on image and/or spatial data descriptive ofdifferent users at different times. The stick figures clustered in aparticular group may be representative of a particular pose taken by thedifferent people. One or more of the pose clusters may map to certainbody parts which are unobstructed from view.

According to various example embodiments, instead of or in addition togenerating a cluster of models representing the positions of the user'sbody at a plurality of times, the analysis module 730 generates acluster of images depicting the user at a plurality of times. In someexample embodiments, instead of or in addition to generating a clusterof models or a cluster of images of the user, the analysis module 730may generate a combination of an image of the user captured at aparticular time and the corresponding model representing a pose of theuser at the particular time. For instance, the combination of the imageand the corresponding model may include an overlaying (e.g., asuperimposition) of the model (e.g., a stick figured representing thepose of the user) over the two-dimensional representation of the user'sbody depicted in the image. The analysis module 730 may cluster thecombination of the image and the corresponding model with one or moreother combinations of other images and other corresponding models. Theanalysis module 730 may store one or more clusters (e.g., of models, ofimages, or of combinations of images and models) in one or more recordsof a database (e.g., the database 126).

FIG. 10 is a diagram illustrating examples of model clusters, accordingto some example embodiments. FIG. 10 includes two rows of combinationsof images of a user and models representing poses of the user. Each ofthe two rows represents example clusters 1010 and 1020. The cluster 1010includes four combinations of images of the user with correspondingmodels that represent particular positions of the body of the user.Similarly, the cluster 1020 includes four combinations of images of theuser with corresponding models that represent particular positions ofthe body of the user. In each combination of an image and a model withinthe clusters 1010 and 1020, the model (e.g., a stick figure) may besuperimposed on its corresponding image of the user. According tocertain example embodiments, the combinations of images and models areclustered together based on the similarity of poses of the user asdepicted in the images and represented in the models corresponding tothe respective images.

As shown in FIG. 10, the combinations of images and models in thecluster 1010 depict the user standing, with the user's arms raised at orabove shoulder level. Accordingly, in some example embodiments, thecluster 1010 may be used for obtaining information about fashions wornby the user on the user's upper body (e.g., based on extracting visualfeatures from an image area that represents the torso of the user) orany other body part that is fully visible within one or more imagesincluded in the cluster 1010.

As shown in FIG. 10, the combinations if images and models in thecluster 1020 depict the user standing, with the user's arms or handspartially obstructing the view of the user's torso. For example, asdepicted in some of the image of the cluster 1020, the user has theuser's arms crossed in front of the user's chest. Accordingly, in someexample embodiments, the cluster 1020 may not be used for obtaininginformation about fashions worn by the user on the user's upper body.However, because the arms and the wrists of the user are closer to thecamera in the images of the cluster 1020, the cluster 1020 may be usedfor obtaining information about fashions worn by the user on the user'sarms or the wrists (e.g., based on extracting visual features from animage area that represents the arms or the wrists of the user) or anyother body part that is fully visible within one or more images includedin the cluster 1020.

FIG. 11 is a flowchart illustrating operations of the preferenceanalysis machine in performing a method 800 of determining fashionpreferences of a user, according to some example embodiments. As shownin FIG. 11, the method 800 may include one or more of method operations1101, 1102, 1103, 1104, and 1105, according to some example embodiments.

Method operation 1101 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 820, in which theanalysis module 730 performs an analysis of the received image and ofthe received set of spatial data. At method operation 1101, the analysismodule 730 obtains (e.g., accesses, receives, selects, or extracts) amodel from the cluster of models and the received image corresponding tothe model. Alternatively, in some example embodiments, the analysismodule 730 obtains a combination of a received image and a correspondingmodel from a cluster of combinations of received images andcorresponding models. The model and the corresponding received image mayrepresent (e.g., describe) a pose of the user at a particular time.

Method operation 1102 may be performed after method operation 1101. Atmethod operation 1102, the analysis module 730 identifies a location inthe receive image that corresponds to the model obtained from thecluster. The location corresponds to a particular body part of the user.The identifying of the location corresponding to the particular bodypart of the user may be based on one or more identifiers of the user'sbone joints. The identifiers of the user's bone joints may be includedin the model representing the pose of the user at a particular time.

Method operation 1103 may be performed after method operation 1102. Atmethod operation 1103, the analysis module 730 extracts an image swatch(e.g., a portion, an area, a square, a rectangle, or a circle) from theidentified location in the image. The image swatch may depict a portionor the entirety of an item worn by the user on the particular body part.In some example embodiments, the analysis module 730 stores theextracted image swatch in a record of a database (e.g., the database126). The analysis module 730 may perform further analysis of one ormore image swatches extracted from one or more received images of theuser.

Method operation 1104 may be performed after method operation 1103. Atmethod operation 1104, the analysis module 730 analyzes the imageswatch. The analysis of the image swatch, in some example embodiments,includes determining one or more attribute-value pairs that characterizethe item worn by the user on the particular body part.

Method operation 1105 may be performed after method operation 1104, inwhich the analysis module 730 analyzes the image swatch. At methodoperation 1105, the analysis module 730 determines one or moreattribute-value pairs that characterize an item worn by the user on theparticular body part of the user. The determining of the one or moreattribute-value pairs may be based on the analysis of the image swatch.For example, based on the analysis of an image swatch extracted from thetorso area of the user depicted in a received image, the analysis module730 may determine that the user wore on the user's upper body an itemcharacterized by the attribute-value pair “color: blue” and by theattribute-value pair “fabric: denim.”

FIG. 12 is a diagram illustrating example images and correspondingexample groups of image swatches extracted from the respective exampleimages, according to some example embodiments. As shown in FIG. 12,images 1210 and 1230 may be sample images obtained by the analysismodule 730 from different clusters of images of the user. The clustersto which the images 1210 and 1230 belong may represent different posesdepicted in the images included in the respective clusters.

During the process of analysis of the image and the model, the analysismodule 730 may identify a particular body part of the user that is fullyvisible (e.g., unobstructed by another body part of object). Theanalysis module 730 may identify an image area (e.g., a location in theimage or a portion of the image) that corresponds to the particularunobstructed body part. For example, as shown in FIG. 12, the analysismodule 730 may identify the image area based on overlaying a boundingbox around a portion of the image that depicts a part of or the entiretyof a particular body part of the user. The analysis module 730 may, insome instances, mark several images areas based on overlaying aplurality of bounding boxes of different colors to identify imageportions that depict distinct (e.g., different) body parts of the user.The identified image area(s) may be used to extract image swatches thatdepict different fashion items worn by the user on different parts ofthe user's body in the particular image.

The analysis module 730 may extract one or more image swatches from theidentified image area(s). For example, as shown in FIG. 12, the analysismodule 730 extracts a first image swatch that corresponds to a firstimage area included within the green bounding box superimposed on theimage 1210, in the area of the user's torso. The analysis module 730 mayextract a second image swatch that corresponds to a second image areaincluded within the yellow bounding box superimposed on the image 1210,in the area of the user's right leg.

Also shown in FIG. 12 are a group of image swatches 1220 that areextracted from the image 1210 and a group of image swatches 1240 thatare extracted from the image 1230. The image swatches in the groups ofimage swatches 1220 and 1240 may be analyzed to determineattribute-value pairs that characterize items worn by the user on one ormore body parts.

FIGS. 13-15 are flowcharts illustrating operations of the preferenceanalysis machine in performing a method 800 of determining fashionpreferences of a user, according to some example embodiments.

As shown in FIG. 13, the method 800 may include one or more of methodoperations 1301 and 1302, according to some example embodiments. Methodoperation 1301 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of method operation 1104, in which theanalysis module 730 analyzes the image swatch. At method operation 1301,the analysis module 730 extracts one or more visual features from theimage swatch.

In some example embodiments, the analysis module 730 stores the one ormore visual features extracted from the image swatch, together or inassociation with an identifier of the image swatch, in a record of adatabase. The one or more visual features extracted from the imageswatch may be used, in some instances, to perform a similarity search ofan inventory of fashion items or a coordination search of the inventoryof fashion items, or both. For example, in a similarity search, one ormore visual features extracted from the image swatch may be comparedwith one or more visual features identified in an image depicting afashion item included in the fashion inventory being searched.

Method operation 1302 may be performed after method operation 1302, inwhich the analysis module 730 extracts one or more visual features fromthe image swatch. At method operation 1302, the analysis module 730determines, based on the visual features extracted from the imageswatch, one or more attribute-value pars that characterize the item wornby the user on the particular body part.

As shown in FIG. 14, the method 800 may include one or more of methodoperations 1401, 1402, 1403, 1404, 1405, and 1406 according to someexample embodiments. Method operation 1401 may be performed as part(e.g., a precursor task, a subroutine, or a portion) of method operation830, in which the preference module 730 identifies a fashion preferenceof the user based on the analysis of the received image and of thereceived set of spatial data. At method operation 1401, the preferencemodule 740 accesses (e.g., receives) a plurality of attribute-valuepairs that characterize one or more items worn by the user during aperiod of time (e.g., at a plurality of times, such as at 10 a.m. everywork day during the month of June).

Method operation 1402 may be performed after the method operation 1401.At method operation 1402, the preference module 740 accesses a pluralityof received images received during the period of time.

Method operation 1403 may be performed after the method operation 1403.At method operation 1404, the preference module 740 determines afrequency of the user wearing items characterized by a particularattribute-value pair of the plurality of the attribute-value pairs,during the period of time. The determining of the frequency may be basedon the plurality of the received images received during the period oftime. The frequency may be represented by a number of days or a numberof times the user wore items characterized by a particularattribute-value pair during the period of time.

Method operation 1404 may be performed after the method operation 1403.At method operation 1405, the preference module 740 identifies a fashionpreference of the user based on the frequency of the user wearing itemscharacterized by the particular attribute-value pair during the periodof time.

In some example embodiments, the preference module 740 may compare thenumber of days of a period of time that the user wore itemscharacterized by a particular attribute-value pair with a thresholdnumber of days. If the number of days of a period of time that the userwore items characterized by the particular attribute-value pair exceedsthe threshold number of days, the preference module 740 identifies theparticular attribute-value pair as representing a fashion preference ofthe user. In some instances, the fashion preference of the user isstored in a record of the database 126 as a general fashion preferenceof the user (e.g., the user likes the color blue or the user likes towear denim). In other instances, the preference of the user is stored ina record of the database 126 as a fashion preference of the userassociated with an identifier of a type of fashion item to which thepreference pertains (e.g., tops, bottoms, footwear, purses, hats, ties,jewelry, etc.).

For example, the preference module 740 may determine that the user worefashion items on the user's upper body that are characterized by theattribute-value pair “color: blue” twenty days out of the last thirtydays. The threshold number of days is eighteen days out of thirty days.The preference module 740 may determine that, because the user woreblue-colored clothing on the user's upper body for more than eighteendays out of the last thirty days, the number of days that the user woreitems characterized by the attribute-value pair “color: blue” exceedsthe threshold number of days. Accordingly, the preference module 740 mayidentify the attribute-value pair “color: blue” as a fashion preferenceof the user.

Method operation 1405 may be performed after method operation 830, inwhich the preference module 730 identifies a fashion preference of theuser based on the analysis of the received image and of the received setof spatial data. At method operation 1405, the search module 750identifies an item that corresponds to the fashion preference of theuser based on (e.g., as part of) a search of an inventory of fashionitems. The preference module 730 may perform a search of the inventoryof fashion items based on the identified fashion preference of the user.The inventory of fashion items may include items available for purchaseon a network-based marketplace.

In some example embodiments, the identifying of the item thatcorresponds to the fashion preference of the user includes performing ofa search of the inventory of fashion items based on the image swatch.The performing of the search may include comparing, according to aparticular search rule, the image swatch and one or more imagesdepicting fashion items within the inventory.

In some example embodiments, the identifying of the item thatcorresponds to the fashion preference of the user includes performing ofa search of the inventory of fashion items based on one or moreattribute-value pairs that characterize the item worn by the user on aparticular body part of the user. The one or more attribute-value pairsmay be determined based on visual features extracted from the imageswatch.

In some example embodiments, the search module 750 may limit the searchto a particular category of items. For example, the search module 750searches for an item identified (e.g., with an identifier or a tagincluded in the metadata of an item listing or of an image of the item)as worn by people of a particular gender (e.g., female or male) or age(e.g., children or adults). According to another example, the searchmodule 750 searches for an item included in a particular category offashion items (e.g., tops, bottoms, hats, footwear, jewelry, etc.).

Method operation 1406 may be performed after method operation 1405. Atmethod operation 1406, the recommendation module 760 generates arecommendation of the item identified during the search of the fashioninventory as corresponding to the fashion preference of the user.

In some example embodiments, the method 800 may further comprisetransmitting (e.g., by the communication module 770) a communication tothe user. The communication may include the generated recommendation ofthe item identified as corresponding to the fashion preference of theuser.

As shown in FIG. 15, the method 800 may include one or more of methodoperations 1501 and 1502, according to some example embodiments. Methodoperation 1501 may be performed as part (e.g., a precursor task, asubroutine, or a portion) of method operation 1405, in which the searchmodule 750 performs a search of an inventory of fashion items based onthe identified fashion preference of the user. At method 1501, thesearch module 750 performs a similarity search of the fashion inventoryand generates similarity search results (e.g., fashion items that aresimilar to an item previously worn by the user).

In some example embodiments, the similarity search is based on an imageswatch. In some instances, the similarity search is also based on one ormore measurements of the user's body. The performing of the similaritysearch may include selecting (e.g., by the search module 750) anextracted image swatch to be used as a query image swatch and matchingthe query image swatch to one or more images of items in the fashioninventory.

In some instances, the search module 750 performs a similarity search ofan inventory of fashion items based on one or more visual featuresextracted from the image swatch. For example, to perform a similaritysearch, the search module 750 compares one or more visual featuresextracted from the image swatch and one or more visual featuresidentified in an image depicting a fashion item included in the fashioninventory being searched.

In some example embodiments, the similarity search is based on anattribute-value pair that characterizes a fashion item previously wornby the user. In some instances, the similarity search is also based onone or more measurements of the user's body. The performing of thesimilarity search may include selecting (e.g., by the search module 750)an attribute-value pair to be used as a query attribute-value pair andidentifying one or more items in the fashion inventory that arecharacterized by the attribute-value pair.

Method operation 1502 may be performed as part (e.g., a precursor task,a subroutine, or a portion) of method operation 1405, in which thesearch module 750 performs a search of an inventory of fashion itemsbased on the identified fashion preference of the user. At method 1502,the search module 750 performs a coordination search of the fashioninventory and generates coordination search results (e.g., fashion itemsthat can be coordinated with an item previously worn by the user).

In some example embodiments, the coordination search is based on animage swatch. In some instances, the coordination search is also basedon one or more measurements of the user's body. The performing of thecoordination search may include selecting (e.g., by the search module750) an extracted image swatch to be used as a query image swatch andidentifying, based on a coordination rule, one or more items in thefashion inventory that can be worn together with the fashion item thatcorresponds to the query image swatch.

In some instances, the search module 750 perform a coordination searchof an inventory of fashion items based on one or more visual featuresextracted from the image swatch. For example, to perform a coordinationsearch, the search module 750 may compare one or more visual featuresextracted from the image swatch and one or more visual featuresidentified in an image depicting a fashion item included in the fashioninventory being searched.

In some instances, one or more coordination rules that specify whatitems can be worn together are generated based on an analysis of aplurality of images of one or more users, that are captured over aperiod of time. The one or more coordination rules may be stored in oneor more records of a database (e.g., the database 126) in associationwith identifiers of one or more users.

In some example embodiments, the coordination search is based on anattribute-value pair that characterizes a fashion item previously wornby the user. In some instances, the coordination search is also based onone or more measurements of the user's body. The performing of thecoordination search may include selecting (e.g., by the search module750) an attribute-value pair to be used as a query attribute-value pairand identifying, based on a coordination rule, one or more items in thefashion inventory that can be worn together with the fashion itempreviously worn by the user and that is characterized by theattribute-value pair.

FIG. 16 is a diagram illustrating example images including query imageswatches and corresponding example search results, according to someexample embodiments. As shown in FIG. 16, the image 1610 may be used toextract two image swatches: a first image swatch marked by a redbounding box overlaid on (e.g., enclosing) a portion of the user's jeansdepicted in the image 1610 and a second image swatch marked by a greenbounding box overlaid on (e.g., enclosing) a portion of the user's whitet-shirt depicted in the image 1610.

The search module 750 may perform a similarity search based on the firstimage swatch extracted from the image 1610. Based on the similaritysearch, the search module 750 may generate one or more similarity searchresults 1620. The search module 750 may perform a coordination searchbased on the second image swatch extracted from the image 1610. Based onthe coordination search, the search module 750 may generate one or morecoordination search results 1630.

Similarly, the search module 750 may perform a similarity search basedon the first image swatch extracted from the image 1640. Based on thesimilarity search, the search module 750 may generate one or moresimilarity search results 1650. The search module 750 may perform acoordination search based on the second image swatch extracted from theimage 1640. Based on the coordination search, the search module 750 maygenerate one or more coordination search results 1660.

According to various example embodiments, one or more of themethodologies described herein may facilitate the determination offashion preferences of users. Moreover, one or more of the methodologiesdescribed herein may facilitate the providing of item recommendations tothe users based on the users' fashion preferences. Hence, one or morethe methodologies described herein may facilitate improving sales of theitems recommended to the users.

When these effects are considered in aggregate, one or more of themethodologies described herein may obviate a need for certain efforts orresources that otherwise would be involved in evaluating images of itemsfor sale online. Efforts expended by a provider of such images (e.g.,the seller) in evaluating such images may be reduced by one or more ofthe methodologies described herein. Computing resources used by one ormore machines, databases, or devices (e.g., within the networkenvironment 300) may similarly be reduced. Examples of such computingresources include processor cycles, network traffic, memory usage, datastorage capacity, power consumption, and cooling capacity.

Example Mobile Device

FIG. 17 is a block diagram illustrating a mobile device 1700, accordingto an example embodiment. The mobile device 1700 may include a processor1702. The processor 1702 may be any of a variety of different types ofcommercially available processors 1702 suitable for mobile devices 1700(for example, an XScale architecture microprocessor, a microprocessorwithout interlocked pipeline stages (MIPS) architecture processor, oranother type of processor 1702). A memory 1704, such as a random accessmemory (RAM), a flash memory, or other type of memory, is typicallyaccessible to the processor 1702. The memory 1704 may be adapted tostore an operating system (OS) 1706, as well as application programs1708, such as a mobile location enabled application that may provideLBSs to a user. The processor 1702 may be coupled, either directly orvia appropriate intermediary hardware, to a display 1710 and to one ormore input/output (I/O) devices 1712, such as a keypad, a touch panelsensor, a microphone, and the like. Similarly, in some embodiments, theprocessor 1702 may be coupled to a transceiver 1714 that interfaces withan antenna 1716. The transceiver 1714 may be configured to both transmitand receive cellular network signals, wireless data signals, or othertypes of signals via the antenna 1716, depending on the nature of themobile device 1700. Further, in some configurations, a GPS receiver 1718may also make use of the antenna 1716 to receive GPS signals.

Modules, Components and Logic

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied (1) on a non-transitorymachine-readable medium or (2) in a transmission signal) orhardware-implemented modules. A hardware-implemented module is atangible unit capable of performing certain operations and may beconfigured or arranged in a certain manner. In example embodiments, oneor more computer systems (e.g., a standalone, client or server computersystem) or one or more processors 1702 may be configured by software(e.g., an application or application portion) as a hardware-implementedmodule that operates to perform certain operations as described herein.

In various embodiments, a hardware-implemented module may be implementedmechanically or electronically. For example, a hardware-implementedmodule may comprise dedicated circuitry or logic that is permanentlyconfigured (e.g., as a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an application-specific integratedcircuit (ASIC)) to perform certain operations. A hardware-implementedmodule may also comprise programmable logic or circuitry (e.g., asencompassed within a general-purpose processor 1702 or otherprogrammable processor 1702) that is temporarily configured by softwareto perform certain operations. It will be appreciated that the decisionto implement a hardware-implemented module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the term “hardware-implemented module” should be understoodto encompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired) or temporarily ortransitorily configured (e.g., programmed) to operate in a certainmanner and/or to perform certain operations described herein.Considering embodiments in which hardware-implemented modules aretemporarily configured (e.g., programmed), each of thehardware-implemented modules need not be configured or instantiated atany one instance in time. For example, where the hardware-implementedmodules comprise a general-purpose processor 1702 configured usingsoftware, the general-purpose processor 1702 may be configured asrespective different hardware-implemented modules at different times.Software may accordingly configure a processor 1702, for example, toconstitute a particular hardware-implemented module at one instance oftime and to constitute a different hardware-implemented module at adifferent instance of time.

Hardware-implemented modules can provide information to, and receiveinformation from, other hardware-implemented modules. Accordingly, thedescribed hardware-implemented modules may be regarded as beingcommunicatively coupled. Where multiple of such hardware-implementedmodules exist contemporaneously, communications may be achieved throughsignal transmission (e.g., over appropriate circuits and buses thatconnect the hardware-implemented modules). In embodiments in whichmultiple hardware-implemented modules are configured or instantiated atdifferent times, communications between such hardware-implementedmodules may be achieved, for example, through the storage and retrievalof information in memory structures to which the multiplehardware-implemented modules have access. For example, onehardware-implemented module may perform an operation, and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware-implemented module may then,at a later time, access the memory device to retrieve and process thestored output. Hardware-implemented modules may also initiatecommunications with input or output devices, and can operate on aresource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1702 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1702 may constitute processor-implementedmodules that operate to perform one or more operations or functions. Themodules referred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods described herein may be at least partiallyprocessor-implemented. For example, at least some of the operations of amethod may be performed by one or more processors 1702 orprocessor-implemented modules. The performance of certain of theoperations may be distributed among the one or more processors 1702 orprocessor-implemented modules, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the one or more processors 1702 or processor-implementedmodules may be located in a single location (e.g., within a homeenvironment, an office environment or as a server farm), while in otherembodiments the one or more processors 1702 or processor-implementedmodules may be distributed across a number of locations.

The one or more processors 1702 may also operate to support performanceof the relevant operations in a “cloud computing” environment or as a“software as a service” (SaaS). For example, at least some of theoperations may be performed by a group of computers (as examples ofmachines including processors), these operations being accessible via anetwork (e.g., the Internet) and via one or more appropriate interfaces(e.g., application program interfaces (APIs)).

Electronic Apparatus and System

Example embodiments may be implemented in digital electronic circuitry,or in computer hardware, firmware, software, or in combinations of them.Example embodiments may be implemented using a computer program product,e.g., a computer program tangibly embodied in an information carrier,e.g., in a machine-readable medium for execution by, or to control theoperation of, data processing apparatus, e.g., a programmable processor1702, a computer, or multiple computers.

A computer program can be written in any form of programming language,including compiled or interpreted languages, and it can be deployed inany form, including as a stand-alone program or as a module, subroutine,or other unit suitable for use in a computing environment. A computerprogram can be deployed to be executed on one computer or on multiplecomputers at one site or distributed across multiple sites andinterconnected by a communication network.

In example embodiments, operations may be performed by one or moreprogrammable processors 1702 executing a computer program to performfunctions by operating on input data and generating output. Methodoperations can also be performed by, and apparatus of exampleembodiments may be implemented as, special purpose logic circuitry,e.g., a field programmable gate array (FPGA) or an application-specificintegrated circuit (ASIC).

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other. Inembodiments deploying a programmable computing system, it will beappreciated that that both hardware and software architectures requireconsideration. Specifically, it will be appreciated that the choice ofwhether to implement certain functionality in permanently configuredhardware (e.g., an ASIC), in temporarily configured hardware (e.g., acombination of software and a programmable processor 1702), or acombination of permanently and temporarily configured hardware may be adesign choice. Below are set out hardware (e.g., machine) and softwarearchitectures that may be deployed, in various example embodiments.

Example Machine Architecture and Machine-Readable Medium

FIG. 18 is a block diagram illustrating components of a machine 1800,according to some example embodiments, able to read instructions 1824from a machine-readable medium 1822 (e.g., a non-transitorymachine-readable medium, a machine-readable storage medium, acomputer-readable storage medium, or any suitable combination thereof)and perform any one or more of the methodologies discussed herein, inwhole or in part. Specifically, FIG. 18 shows the machine 1800 in theexample form of a computer system (e.g., a computer) within which theinstructions 1824 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 1800 toperform any one or more of the methodologies discussed herein may beexecuted, in whole or in part.

In alternative embodiments, the machine 1800 operates as a standalonedevice or may be connected (e.g., networked) to other machines. In anetworked deployment, the machine 1800 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a distributed (e.g., peer-to-peer)network environment. The machine 1800 may be a server computer, a clientcomputer, a personal computer (PC), a tablet computer, a laptopcomputer, a netbook, a cellular telephone, a smartphone, a set-top box(STB), a personal digital assistant (PDA), a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1824, sequentially or otherwise, that specifyactions to be taken by that machine. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executethe instructions 1824 to perform all or part of any one or more of themethodologies discussed herein.

The machine 1800 includes a processor 1802 (e.g., a central processingunit (CPU), a graphics processing unit (GPU), a digital signal processor(DSP), an application specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), or any suitable combinationthereof), a main memory 1804, and a static memory 1806, which areconfigured to communicate with each other via a bus 1808. The processor1802 may contain microcircuits that are configurable, temporarily orpermanently, by some or all of the instructions 1824 such that theprocessor 1802 is configurable to perform any one or more of themethodologies described herein, in whole or in part. For example, a setof one or more microcircuits of the processor 1802 may be configurableto execute one or more modules (e.g., software modules) describedherein.

The machine 1800 may further include a graphics display 1810 (e.g., aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, a cathode ray tube (CRT), orany other display capable of displaying graphics or video). The machine1800 may also include an alphanumeric input device 1812 (e.g., akeyboard or keypad), a cursor control device 1814 (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, an eye trackingdevice, or other pointing instrument), a storage unit 1816, an audiogeneration device 1818 (e.g., a sound card, an amplifier, a speaker, aheadphone jack, or any suitable combination thereof), and a networkinterface device 1820.

The storage unit 1816 includes the machine-readable medium 1822 (e.g., atangible and non-transitory machine-readable storage medium) on whichare stored the instructions 1824 embodying any one or more of themethodologies or functions described herein. The instructions 1824 mayalso reside, completely or at least partially, within the main memory1804, within the processor 1802 (e.g., within the processor's cachememory), or both, before or during execution thereof by the machine1800. Accordingly, the main memory 1804 and the processor 1802 may beconsidered machine-readable media (e.g., tangible and non-transitorymachine-readable media). The instructions 1824 may be transmitted orreceived over the network 1826 via the network interface device 1820.For example, the network interface device 1820 may communicate theinstructions 1824 using any one or more transfer protocols (e.g.,hypertext transfer protocol (HTTP)).

In some example embodiments, the machine 1800 may be a portablecomputing device, such as a smart phone or tablet computer, and have oneor more additional input components 1830 (e.g., sensors or gauges).Examples of such input components 1830 include an image input component(e.g., one or more cameras), an audio input component (e.g., amicrophone), a direction input component (e.g., a compass), a locationinput component (e.g., a global positioning system (GPS) receiver), anorientation component (e.g., a gyroscope), a motion detection component(e.g., one or more accelerometers), an altitude detection component(e.g., an altimeter), and a gas detection component (e.g., a gassensor). Inputs harvested by any one or more of these input componentsmay be accessible and available for use by any of the modules describedherein.

As used herein, the term “memory” refers to a machine-readable mediumable to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1822 is shown in an example embodiment to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storeinstructions. The term “machine-readable medium” shall also be taken toinclude any medium, or combination of multiple media, that is capable ofstoring the instructions 1824 for execution by the machine 1800, suchthat the instructions 1824, when executed by one or more processors ofthe machine 1800 (e.g., processor 1802), cause the machine 1800 toperform any one or more of the methodologies described herein, in wholeor in part. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as cloud-based storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” shall accordingly be taken to include,but not be limited to, one or more tangible (e.g., non-transitory) datarepositories in the form of a solid-state memory, an optical medium, amagnetic medium, or any suitable combination thereof.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute softwaremodules (e.g., code stored or otherwise embodied on a machine-readablemedium or in a transmission medium), hardware modules, or any suitablecombination thereof. A “hardware module” is a tangible (e.g.,non-transitory) unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware modules of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as a fieldprogrammable gate array (FPGA) or an ASIC. A hardware module may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwaremodule may include software encompassed within a general-purposeprocessor or other programmable processor. It will be appreciated thatthe decision to implement a hardware module mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, and such a tangible entity may bephysically constructed, permanently configured (e.g., hardwired), ortemporarily configured (e.g., programmed) to operate in a certain manneror to perform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Software(e.g., a software module) may accordingly configure one or moreprocessors, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, a processor being an example of hardware. Forexample, at least some of the operations of a method may be performed byone or more processors or processor-implemented modules. As used herein,“processor-implemented module” refers to a hardware module in which thehardware includes one or more processors. Moreover, the one or moreprocessors may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines includingprocessors), with these operations being accessible via a network (e.g.,the Internet) and via one or more appropriate interfaces (e.g., anapplication program interface (API)).

The performance of certain operations may be distributed among the oneor more processors, not only residing within a single machine, butdeployed across a number of machines. In some example embodiments, theone or more processors or processor-implemented modules may be locatedin a single geographic location (e.g., within a home environment, anoffice environment, or a server farm). In other example embodiments, theone or more processors or processor-implemented modules may bedistributed across a number of geographic locations.

Some portions of the subject matter discussed herein may be presented interms of algorithms or symbolic representations of operations on datastored as bits or binary digital signals within a machine memory (e.g.,a computer memory). Such algorithms or symbolic representations areexamples of techniques used by those of ordinary skill in the dataprocessing arts to convey the substance of their work to others skilledin the art. As used herein, an “algorithm” is a self-consistent sequenceof operations or similar processing leading to a desired result. In thiscontext, algorithms and operations involve physical manipulation ofphysical quantities. Typically, but not necessarily, such quantities maytake the form of electrical, magnetic, or optical signals capable ofbeing stored, accessed, transferred, combined, compared, or otherwisemanipulated by a machine. It is convenient at times, principally forreasons of common usage, to refer to such signals using words such as“data,” “content,” “bits,” “values,” “elements,” “symbols,”“characters,” “terms,” “numbers,” “numerals,” or the like. These words,however, are merely convenient labels and are to be associated withappropriate physical quantities.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or any suitable combination thereof), registers, orother machine components that receive, store, transmit, or displayinformation. Furthermore, unless specifically stated otherwise, theterms “a” or “an” are herein used, as is common in patent documents, toinclude one or more than one instance. Finally, as used herein, theconjunction “or” refers to a non-exclusive “or,” unless specificallystated otherwise.

1-20. (canceled)
 21. A method comprising: receiving an image of a bodyof a user, the image captured at a point in time; performing an analysisof the image, the analysis comprising extracting an image swatch fromthe image, the image swatch depicting one or more visual features of anitem worn by the user at the point in time; identifying, based on theone or more visual features, one or more fashion items having one ormore pre-identified visual features that match at least one of the oneor more visual features of the item worn by the user, the one or morefashion items from an inventory of fashion items stored in a databaserecord; and transmitting a communication to a device associated with theuser, the communication including an indication of the one or morefashion items.
 22. The method of claim 21, wherein the identifyingfurther comprises identifying the one or more fashion items having anumber of the pre-identified visual features that match at least one ofthe one or more visual features of the item worn by the user, whereinthe number exceeds a threshold value.
 23. The method of claim 21,further comprising: identifying, based on the image swatch and from theinventory of fashion items, a coordinating fashion item that coordinateswith the item worn by the user as defined by one or more coordinationrules.
 24. The method of claim 21, further comprising: receiving a setof spatial data indicating shape and/or dimension data of the body, theset of spatial data captured at the point in time; wherein theidentifying the one or more fashion items is further based on the shapeand/or dimension data of the body.
 25. The method of claim 24, furthercomprising: generating, based on the image and the spatial data, a modelof the user that represents the body of the user and the item worn bythe user.
 26. The method of claim 25, further comprising: determining,based on an analysis of the item worn by the user and the model, anattribute-value pair that characterizes the item worn by the user;wherein the identifying the one or more fashion items is further basedon the one or more fashion items characterized by the attribute-valuepair of the item worn by the user.
 27. The method of claim 21, furthercomprising: determining an attribute-value pair that characterizes theitem worn by the user; wherein the identifying the one or more fashionitems is further based on the one or more fashion items characterized bythe attribute-value pair of the item worn by the user.
 28. A systemcomprising: a storage device storing instructions; and a hardwareprocessor configured by the instructions to perform operationscomprising: receiving an image of a body of a user, the image capturedat a point in time; performing an analysis of the image, the analysiscomprising extracting an image swatch from the image, the image swatchdepicting one or more visual features of an item worn by the user at thepoint in time; identifying, based on the one or more visual features,one or more fashion items having one or more pre-identified visualfeatures that match at least one of the one or more visual features ofthe item worn by the user, the one or more fashion items from aninventory of fashion items stored in a database record; and transmittinga communication to a device associated with the user, the communicationincluding an indication of the one or more fashion items.
 29. The systemof claim 28, wherein the identifying further comprises identifying theone or more fashion items having a number of the pre-identified visualfeatures that match at least one of the one or more visual features ofthe item worn by the user, wherein the number exceeds a threshold value.30. The system of claim 28, wherein the operations further comprise:identifying, based on the image swatch and from the inventory of fashionitems, a coordinating fashion item that coordinates with the item wornby the user as defined by one or more coordination rules.
 31. The systemof claim 28, wherein the operations further comprise: receiving a set ofspatial data indicating shape and/or dimension data of the body, the setof spatial data captured at the point in time; wherein the identifyingthe one or more fashion items is further based on the shape and/ordimension data of the body.
 32. The system of claim 31, wherein theoperations further comprise: generating, based on the image and thespatial data, a model of the user that represents the body of the userand the item worn by the user.
 33. The system of claim 32, wherein theoperations further comprise: determining, based on an analysis of theitem worn by the user and the model, an attribute-value pair thatcharacterizes the item worn by the user; wherein the identifying the oneor more fashion items is further based on the one or more fashion itemscharacterized by the attribute-value pair of the item worn by the user.34. The system of claim 28, wherein the operations further comprise:determining an attribute-value pair that characterizes the item worn bythe user; wherein the identifying the one or more fashion items isfurther based on the one or more fashion items characterized by theattribute-value pair of the item worn by the user.
 35. A non-transitorymachine-readable storage medium storing instructions that, when executedby one or more hardware processors, cause the one or more hardwareprocessors to perform operations comprising: receiving an image of abody of a user, the image captured at a point in time; performing ananalysis of the image, the analysis comprising extracting an imageswatch from the image, the image swatch depicting one or more visualfeatures of an item worn by the user at the point in time; identifying,based on the one or more visual features, one or more fashion itemshaving one or more pre-identified visual features that match at leastone of the one or more visual features of the item worn by the user, theone or more fashion items from an inventory of fashion items stored in adatabase record; and transmitting a communication to a device associatedwith the user, the communication including an indication of the one ormore fashion items.